Statistics & Mathematics for Data Science & Data Analytics

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Overview

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course equips you with essential statistical and mathematical tools to become proficient in data science and analytics. You will learn key concepts in descriptive statistics, probability theory, regression analysis, hypothesis testing, and more. By the end of the course, you will have a deep understanding of how statistical methods can be applied to solve real-world data problems and enhance data-driven decision-making. The course begins with an introduction to the basics of descriptive statistics, such as measures of central tendency, dispersion, and the differences between sample and population data. You will then explore distributions, including the normal distribution and Z-scores, and how to apply them in various scenarios. The journey continues with probability theory, where you will tackle concepts like Bayes' theorem, expected value, and the central limit theorem, building a solid foundation for statistical analysis. Next, you will dive into hypothesis testing and learn how to perform tests like t-tests and proportion testing. You will also understand the significance of confidence intervals, the margin of error, and Type I and Type II errors. The regression section teaches you how to predict data values using linear regression, explore correlation coefficients, and analyze model accuracy with metrics such as MSE and RMSE. This course is ideal for aspiring data scientists, analysts, and anyone who wants to use statistics to interpret data. No prior knowledge of statistics is required, though familiarity with basic mathematics will be helpful. The course is structured to be engaging and practical, offering exercises and real-world applications that allow you to practice your skills.

Syllabus

Let's Get Started

In this module, we will introduce you to the overall course structure, key learning outcomes, and the mindset required to thrive in data science. You'll gain clarity on what to expect and how to approach the course strategically. This foundation sets the tone for an efficient and impactful learning journey.

Descriptive Statistics

In this module, we will explore the foundational tools of descriptive statistics, including mean, median, mode, and measures of spread like range and standard deviation. You'll also practice interpreting real-world data distributions and grasp the significance of statistical moments. This section lays the groundwork for making sense of raw data.

Distributions

In this module, we will dive into the concept of distributions, focusing on the normal distribution and Z-scores. Through theory and practice, you'll learn how to interpret standardized scores and recognize distribution patterns in datasets. These insights are key to deeper statistical understanding.

Probability Theory

In this module, we will transition from descriptive statistics to probability theory, covering foundational rules, key theorems, and probability distributions. You’ll build strong analytical skills through hands-on practice and explore concepts like expected value and the central limit theorem. Mastery of this section is essential for predictive modeling.

Hypothesis Testing

In this module, we will introduce you to inferential statistics through hypothesis testing. You'll learn how to draw conclusions about populations, calculate sample sizes, and test assumptions using statistical methods. This section empowers you to make data-driven decisions with confidence.

Regressions

In this module, we will explore regression analysis as a predictive tool, starting with simple linear regression. You'll learn to quantify relationships between variables and evaluate the quality of your models. Real-world practice exercises will reinforce key statistical techniques.

Advanced Regression and Machine Learning Algorithms

In this module, we will take a deeper dive into advanced regression techniques and machine learning algorithms. From multiple linear regression to decision trees and random forests, you’ll explore predictive modeling in more dynamic environments. You'll also learn to handle common data challenges like overfitting and missing data.

ANOVA (Analysis of Variance)

In this module, we will explore ANOVA, a powerful statistical tool for comparing group means. You'll learn to analyze the influence of single and multiple factors, apply F-distribution, and draw valid conclusions from your data. This is a critical step for mastering inferential statistics.

Wrap Up

In this module, we will conclude the course with a final wrap-up, reflecting on what you've accomplished and the knowledge you've built. You’ll be guided on how to take your learning forward and apply these concepts in real-world data analytics and data science projects.